From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence

In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has captivated the research community, leading to debates about its application in healthcare, mainly due to concerns about transparency and related issues. Meanwhile, concerns about the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied, and decision-makers often fail to consider the significance of generative AI. In this paper, we propose GREAT PLEA ethical principles, encompassing governance, reliability, equity, accountability, traceability, privacy, lawfulness, empathy, and autonomy, for generative AI in healthcare. We aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI in healthcare

Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare

Abstract In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the “GREAT PLEA” ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Autonomy for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice

Improving clinician decisions and communication in critical care using novel information technology

Introduction The electronic medical record (EMR) is presumed to support clinician decisions by documenting and retrieving patient information. Research shows that the EMR variably affects patient care and clinical decision making. The way information is presented likely has a significant impact on this variability. Well-designed representations of salient information can make a task easier by integrating information in useful patterns that clinicians use to make improved clinical judgments and decisions. Using Cognitive Systems Engineering methods, our research team developed a novel health information technology (NHIT) that interfaces with the EMR to display salient clinical information and enabled communication with a dedicated text-messaging feature. The software allows clinicians to customize displays according to their role and information needs. Here we present results of usability and validation assessments of the NHIT. Materials and Methods Our subjects were physicians, nurses, respiratory therapists, and physician trainees. Two arms of this study were conducted, a usability assessment and then a validation assessment. The usability assessment was a computer-based simulation using deceased patient data. After a brief five-minute orientation, the usability assessment measured individual clinician performance of typical tasks in two clinical scenarios using the NHIT. The clinical scenarios included patient admission to the unit and patient readiness for surgery. We evaluated clinician perspective about the NHIT after completing tasks using 7-point Likert scale surveys. In the usability assessment, the primary outcome was participant perceptions about the system’s ease of use compared to the legacy system. A subsequent cross-over, validation assessment compared performance of two clinical teams during simulated care scenarios: one using only the legacy IT system and one using the NHIT in addition to the legacy IT system. We oriented both teams to the NHIT during a 1-hour session on the night before the first scenario. Scenarios were conducted using high-fidelity simulation in a real burn intensive care unit room. We used observations, task completion times, semi-structured interviews, and surveys to compare user decisions and perceptions about their performance. The primary outcome for the validation assessment was time to reach accurate (correct) decision points. Results During the usability assessment, clinicians were able to complete all tasks requested. Clinicians reported the NHIT was easier to use and the novel information display allowed for easier data interpretation compared to subject recollection of the legacy EMR. In the validation assessment, a more junior team of clinicians using the NHIT arrived at accurate diagnoses and decision points at similar times as a more experienced team. Both teams noted improved communication between team members when using the NHIT and overall rated the NHIT as easier to use than the legacy EMR, especially with respect to finding information. Conclusions The primary findings of these assessments are that clinicians found the NHIT easy to use despite minimal training and experience and that it did not degrade clinician efficiency or decision-making accuracy. These findings are in contrast to common user experiences when introduced to new EMRs in clinical practice

Mechanical Ventilation Strategies in the Critically Ill Burn Patient: A Practical Review for Clinicians

Burn patients are a unique population when considering strategies for ventilatory support. Frequent surgical operations, inhalation injury, pneumonia, and long durations of mechanical ventilation add to the challenging physiology of severe burn injury. We aim to provide a practical and evidence-based review of mechanical ventilation strategies for the critically ill burn patient that is tailored to the bedside clinician